



"""simple docstring"""
from __future__ import annotations




class       snake_case_    :






							def __init__( self      ,  a_ = 0   ):
									a_  :   Dict			    =  key






							def snake_case_	( self      ,  a_      ,  a_   ):
									assert isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   ) and isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

									a_  :   Optional[Any]			    =  key or self.__key or 1

									# make sure key is an appropriate size
									key %= 2_5_5

									return [chr(ord(__SCREAMING_SNAKE_CASE   ) ^ key   ) for ch in content]






							def snake_case_	( self      ,  a_      ,  a_   ):
									assert isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   ) and isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

									a_  :   Union[str, Any]			    =  key or self.__key or 1

									# make sure key is an appropriate size
									key %= 2_5_5

									return [chr(ord(__SCREAMING_SNAKE_CASE   ) ^ key   ) for ch in content]






							def snake_case_	( self      ,  a_      ,  a_ = 0   ):
									assert isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   ) and isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

									a_  :   Union[str, Any]			    =  key or self.__key or 1

									# make sure key can be any size
									while key > 2_5_5:
											key -= 2_5_5

									# This will be returned
									a_  :   List[str]			    =  ""

									for ch in content:
											ans += chr(ord(__SCREAMING_SNAKE_CASE   ) ^ key   )

									return ans






							def snake_case_	( self      ,  a_      ,  a_ = 0   ):
									assert isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   ) and isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

									a_  :   Dict			    =  key or self.__key or 1

									# make sure key can be any size
									while key > 2_5_5:
											key -= 2_5_5

									# This will be returned
									a_  :   Optional[int]			    =  ""

									for ch in content:
											ans += chr(ord(__SCREAMING_SNAKE_CASE   ) ^ key   )

									return ans






							def snake_case_	( self      ,  a_      ,  a_ = 0   ):
									assert isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   ) and isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

									try:
											with open(__SCREAMING_SNAKE_CASE   ) as fin, open("encrypt.out"      ,  "w+"   ) as fout:
													# actual encrypt-process
													for line in fin:
															fout.write(self.encrypt_string(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )   )

									except OSError:
											return False

									return True






							def snake_case_	( self      ,  a_      ,  a_   ):
									assert isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   ) and isinstance(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )

									try:
											with open(__SCREAMING_SNAKE_CASE   ) as fin, open("decrypt.out"      ,  "w+"   ) as fout:
													# actual encrypt-process
													for line in fin:
															fout.write(self.decrypt_string(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE   )   )

									except OSError:
											return False

									return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")







"""simple docstring"""





import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
_SCREAMING_SNAKE_CASE   :							int          =		pd.read_csv(
    '''https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'''
    '''position_salaries.csv'''
)
_SCREAMING_SNAKE_CASE   :							List[Any]          =		dataset.iloc[:, 1:2].values
_SCREAMING_SNAKE_CASE   :							str          =		dataset.iloc[:, 2].values


_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE       ,				_SCREAMING_SNAKE_CASE   :							Dict          =		train_test_split(X, y, test_size=0.2, random_state=0)


_SCREAMING_SNAKE_CASE   :							Optional[int]          =		PolynomialFeatures(degree=4)
_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		poly_reg.fit_transform(X)
_SCREAMING_SNAKE_CASE   :							List[str]          =		LinearRegression()
pol_reg.fit(X_poly, y)







def 	lowerCamelCase__			(				) ->							List[str]:
  plt.scatter(_lowerCamelCase						,							_lowerCamelCase						,							color='red'					)
  plt.plot(_lowerCamelCase						,							pol_reg.predict(poly_reg.fit_transform(_lowerCamelCase					)					)						,							color='blue'					)
  plt.title('Truth or Bluff (Linear Regression)'					)
  plt.xlabel('Position level'					)
  plt.ylabel('Salary'					)
  plt.show()


if __name__ == "__main__":
  viz_polymonial()

  # Predicting a new result with Polymonial Regression
  pol_reg.predict(poly_reg.fit_transform([[5.5]]))
  # output should be 132148.43750003




